Hybrid Fuel Cell/Battery Intelligent Energy Management system for UAV

Elkerdany, Mohamed Sameh; Safwat, Ibrahim M.; Yossef, Ahmed Medhat Mohamed; El-Khatib, Mohamed

doi:10.1109/icenco49778.2020.9357393

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…In [31], an intelligent BMS was proposed, wherein intelligent control was used. Another logical the use of fuzzy logic [32]. This paper also presents an intelligent software approach.…”

Section: A Related Workmentioning

confidence: 99%

Design, Implementation, and Deployment of Modular Battery Management System for IIoT-Based Applications

2022

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This paper proposes design and implementation of a battery management system (BMS) for the industrial internet of things (IIoT) enabled applications. The hardware and software development of this BMS is briefly presented in this paper. In terms of hardware development, the presented BMS have modular topology and has 1) high fault tolerance and 2) has exceptionally flexible deployment owing to its topology having multiple local management units (LMUs) connected to a central management unit (CMU). This hardware design approach aims to address the overall design efficiency and cost trade-off of BMS deployment. The hardware design efficiency is tested using actual deployment. In terms of fault tolerance using 1 -3 LMUs at fault, the voltage monitoring accuracy is maintained for each LMUs. An average of 0.00017 V, 0.0008 V and 0.001 V voltage difference is yielded for 1, 2, and 3 modules at fault respectively. Additionally, core BMS sub-circuit is tested to verify hardware design efficiency such as the DC-to-DC converter which yielded 92.74%. Furthermore, the CMU is integrated with a wireless communication circuit that enables IIoTbased applications such as the emerging edge-based, and a plethora of intelligent deployment. In terms of software, the presented BMS aims to realize state-of-the-art processing through IIoT based approach. For software testing and verification, the BMS is deployed to an unmanned ground vehicle (UGV). The signal stability is tested for UGV based application at a 3500s deployment time whereas an average of 0.0010V voltage difference is yielded. This is verified using time markers which is further analyzed using softwarebased signal processing and acquisition simulation. Concisely, the proposed BMS aims to converge IIoT applications to its actual deployment. The proposed BMS is designed, implemented, and successfully deployed to test its viability both in the simulation platform and actual deployment.INDEX TERMS Battery management system, edge processing, industrial IoT, lithium-ion, modular architecture.

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“…In [31], an intelligent BMS was proposed, wherein intelligent control was used. Another logical the use of fuzzy logic [32]. This paper also presents an intelligent software approach.…”

Section: A Related Workmentioning

confidence: 99%

Design, Implementation, and Deployment of Modular Battery Management System for IIoT-Based Applications

2022

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“…As shown in the paper, the storage component can be charged or discharged through the rotor-side converter by increasing or decreasing the speed of the generator. Energy management techniques such as those presented in previous studies [22,23] may be applicable.…”

Section: Introductionmentioning

confidence: 99%

Induction motor control via the rotor of a doubly fed induction generator

Hossain,

Bodson

2023

Asian Journal of Control

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A novel configuration is studied where a doubly fed induction generator provides power to a squirrel‐cage induction motor, and the speed of the motor is controlled through the rotor‐side converter of the generator. Such topology could have applications in hybrid‐electric propulsion and would require fewer and smaller power electronic converters than a system with an intermediate DC bus. An integrated motor/generator set model is developed to reflect the tightly connected dynamics of the combined electric machine. To simplify the derivations, a representation in complex variables is used. However, the control algorithm is based on proven field‐oriented control principles for induction motors. Feedforward and feedback control algorithms of the induction motor integrated with the doubly fed induction generator are proposed, together with two methods to limit the rotor currents of the generator. Experimental data show excellent tracking performance of speed references, demonstrating the validity of the control methods proposed. Concepts are also presented for the management of a small energy storage component.

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